We train our method on ShapeNet [1]} data set. In order to achieve better generalization we use objects from all classes. In each step the object class and corresponding object from it selected randomly. We follow this strategy for both pre-training and training phases. To assess our agent we conduct two different comparisons with different baselines. In the first experiment we compare our method with the results of slices based surface reconstruction methods (Bermano et al.[2]} and Zou et al. [3]}), point cloud based method [4]} and baseline method where we use not projection images. Both slices based approaches are build on the level-set extraction of some indicator function. Additionally, Zou et al. [3]} searches for an optimal topology which complies with the user prescribed genus. From this perspective Zou's method is similar to our method. However, our approaches towards solving branching and connectivity problems is completely different. Where we focus on parsing 3D shape into smaller regions which increases reconstruction accuracy. While Bermano's method does not search for the optimal topology it additionally proposes a solution to unknown and multi-labelled regions. Figure REF  illustrates the result of comparison of these methods on shape reconstruction from the given contours. In this example, 3 out of 5 contours are downloaded from the Internet while 2 others represent ShapeNet objects. Here we consider two different settings of our method for 3D shape reconstruction: sparse input which represent planar cross sections shown in the Figure REF , and dense input with two times more slices. As it can be seen from the Figure REF , our method captures difficult topology and geometries more accurately for both settings. Also, the quality of reconstruction for all objects are similar for both IoU and CD metrics despite the model was trained only on the ShapeNet data set (Table REF ). It should also be noted that none of these methods generated the results for dense inputs. While our method effectively works with dense and as well as more sparse input. This indicates generalization capability of our method. This should be noted that our IoU calculation takes into account surface correspondence only.
